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How can neuromorphic hardware attain brain-like functional capabilities?

National science Review 2024年第5期11卷 11-13页

作者： Wolfgang Maass Computer Science and Biomedical Engineering Graz University of Technology

Research on neuromorphic computing is driven by the vision that we can emulate brain-like computing capability, learning capability, and energy-efficiency in novel hardware. Unfortunately, this vision has so far been pursued in a half-hearted manner. Most current neuromorphic hardware (NMHW) employs brain-like spiking neurons instead of standard artificial neurons.

关键词： capabilities attain brain-like functional hardware neuromorphic

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Improvement of Prior Image for Metal Artifact Reduction of Computed Tomography

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Journal of Shanghai Jiaotong University (science) 2025年第3期30卷 446-451页

作者： Sun, Wenwu Zhuang, Tiange Chen, Siping School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan411201 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Biomedical Engineering Shenzhen University Guangdong Shenzhen518060 China

It is not easy to reduce the metal artifacts of computed tomography images. However, the pixel values inside the metal artifact regions vary smoothly, while those on the borders of the metal and the bone regions vary sharply. When the Canny operation by adaptive thresholding is conducted on the raw image, the almost continuous edges can be formed obviously on the borders of the metal and the bone regions, but this kind of information cannot be formed for the metal artifact regions. In this paper, by searching the closed areas formed by the border edges of the bone regions in the Canny image, the metal artifact regions, which are very difficult to discriminate only by intensity thresholding, can be excluded effectively. A novel prior image-based method is thus developed for metal artifact reduction. The experiments demonstrate that the proposed method can be realized easily and reduce the metal artifacts effectively even if multiple large metal objects exist simultaneously in the image. The method is suitable for the clinical application. © Shanghai Jiao Tong University 2023.

关键词： computerized tomography

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A survey on augmenting knowledge graphs (KGs) with large language models (LLMs): models, evaluation metrics, benchmarks, and challenges

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Discover Artificial Intelligence 2024年第1期4卷 1-28页

作者： Ibrahim, Nourhan Aboulela, Samar Ibrahim, Ahmed Kashef, Rasha Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoON Canada Faculty of Engineering Alexandria University Alexandria Egypt Computer Science Western University LondonON Canada

Integrating Large Language Models (LLMs) with Knowledge Graphs (KGs) enhances the interpretability and performance of AI systems. This research comprehensively analyzes this integration, classifying approaches into three fundamental paradigms: KG-augmented LLMs, LLM-augmented KGs, and synergized frameworks. The evaluation examines each paradigm’s methodology, strengths, drawbacks, and practical applications in real-life scenarios. The findings highlight the substantial impact of these integrations in fundamentally improving real-time data analysis, efficient decision-making, and promoting innovation across various domains. In this paper, we also describe essential evaluation metrics and benchmarks for assessing the performance of these integrations, addressing challenges like scalability and computational overhead, and providing potential solutions. This comprehensive analysis underscores the profound impact of these integrations on improving real-time data analysis, enhancing decision-making efficiency, and fostering innovation across various domains. © The Author(s) 2024.

关键词： Knowledge graph

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Inspires effective alternatives to backpropagation:predictive coding helps understand and build learning

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Neural Regeneration Research 2025年第11期20卷 3215-3216页

作者： Zhenghua Xu Miao Yu Yuhang Song School of Health Sciences and Biomedical Engineering Hebei University of TechnologyTianjinChina Department of Computer Science University of OxfordOxfordUK

Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human *** enable learning by brain and machine,it is essential to accurately identify and correct the prediction errors,referred to as credit assignment(Lillicrap et al.,2020).It is critical to develop artificial intelligence by understanding how the brain deals with credit assignment in neuroscience.

关键词： assignment learning enable

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Harmony in transcripts: a systematic literature review of transcriptome-wide association studies

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Journal of engineering and Applied science 2024年第1期71卷 1-15页

作者： Mashhour, Mahinaz A. Kandil, Ahmed Hisham AbdElwahed, Manal Mabrouk, Mai S. Biomedical Engineering Department Misr University for Science and Technology Giza Egypt Systems and Biomedical Engineering Department Cairo University Giza Egypt School of Information Technology and Computer Science Nile University Sheikh Zayed City Egypt

Transcriptome-wide association studies (TWAS) goal is to better understand the etiology of diseases and develop preventative and therapeutic approaches by examining the connections between genetic variants and phenotypes while overcoming the limitations of the genome-wide association study (GWAS). It is a valuable complement to GWAS, reducing the negative effects of multiple tests and enabling a more thorough investigation of gene expression patterns in various tissues. A systematic review is presented in this paper to identify articles that utilize TWAS to understand the genetic factors behind complex diseases. A detailed selection process was carried out using standard PRISMA criteria to select relevant articles for the review. Twenty-five articles passed the inclusion criteria and were selected for additional review. The studies cover a diverse range of disorders, including Tourette’s syndrome, Alzheimer’s disease, rheumatoid arthritis, and major depression. Leveraging gene expression data from different tissues and populations, these investigations successfully identified novel genes and pathways associated with the studied conditions. The collective findings highlight the transformative impact of integrative genomics in advancing our understanding of complex diseases, providing insights into potential therapeutic targets, and laying the foundation for precision medicine approaches. © The Author(s) 2024.

关键词： Genome

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A Novel Lightweight Image Encryption Scheme

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computers, Materials & Continua 2023年第4期75卷 2137-2153页

作者： Rawia Abdulla Mohammed Maisa’a Abid Ali Khodher Ashwak Alabaichi Department of Computer Science University of TechnologyBaghdadIraq Department of Biomedical Engineering University of KerbalaKerbalaIraq

Encryption algorithms are one of the methods to protect dataduring its transmission through an unsafe transmission medium. But encryptionmethods need a lot of time during encryption and decryption, so itis necessary to find encryption algorithms that consume little time whilepreserving the security of the data. In this paper, more than one algorithmwas combined to obtain high security with a short implementation time. Achaotic system, DNA computing, and Salsa20 were combined. A proposed5D chaos system was used to generate more robust keys in a Salsa algorithmand DNA computing. Also, the confusion is performed using a new *** proposed chaos system achieves three positive Lyapunov *** results demonstrate of the proposed scheme has a sufficient peak signalto-noise ratio, a low correlation, and a large key space. These factors makeit more efficient than its classical counterpart and can resist statistical anddifferential attacks. The number of changing pixel rates (NPCR) and theunified averaged changed intensity (UACI) values were 0.99710 and UACI33.68. The entropy oscillates from 7.9965 to 7.9982 for the tested encryptedimages. The suggested approach is resistant to heavy attacks and takes lesstime to execute than previously discussed methods, making it an efficient,lightweight image encryption scheme. The method provides lower correlationcoefficients than other methods, another indicator of an efficient imageencryption system. Even though the proposed scheme has useful applicationsin image transmission, it still requires profound improvement in implementingthe high-intelligence scheme and verifying its feasibility on devices with theInternet of Things (IoT) enabled.

关键词： Chaotic system lightweight confusion diffusion encryption Salsa20

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Automating skin cancer screening: a deep learning

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Journal of engineering and Applied science 2025年第1期72卷 1-18页

作者： Rashad, Nada M. Abdelnapi, Noha MM. Seddik, Ahmed F. Sayedelahl, M.A. Department of Computer Science Faculty of Computers and Information Suez University Suez43221 Egypt Biomedical Engineering Department Faculty of Engineering Helwan University Cairo11795 Egypt Department of Computer Science Faculty of Computers and Information Damanhour University Damanhour22511 Egypt

Skin cancer presents in various forms, including squamous cell carcinoma (SCC), basal cell carcinoma (BCC), and melanoma. Established risk factors include ultraviolet (UV) radiation exposure from solar or artificial sources, lighter skin pigmentation, a history of sunburns, and a family history of the disease. Early detection and prompt intervention are crucial for achieving a favorable prognosis. Traditionally, treatment modalities include surgery, radiation therapy, and chemotherapy. Recent advancements in immunotherapy have revolutionized skin cancer diagnosis, but manual identification remains time-consuming. Artificial intelligence (AI) has shown potential in skin cancer classification, leading to automated screening methods. To support dermatologists, we improved the model for classifying images. This model is able to recognize seven different kinds of skin lesions. On the ISIC dataset, an analysis has been done. This study offers a novel approach to early skin cancer diagnosis based on image processing. Our approach leverages the high accuracy of a specific convolutional neural network architecture, utilizing transfer learning with pre-trained data to further enhance detection performance. Our findings demonstrate that the employed ResNet-50 transfer learning model achieves a remarkable accuracy of 97%, while ResNet50 without augmentation gives an accuracy of 81.57% and an F1-score of 75.75%. © The Author(s) 2025.

关键词： Convolutional neural networks

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A deep learning framework for enhanced mass spectrometry data analysis and biomarker screening

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computer Methods in Biomechanics and biomedical engineering 2025年 1-13页

作者： Zhang, Shuyu Li, Zhiyu Peng, Weili Chen, Yuanyuan Wu, Yao Machine Intelligence Lab College of Computer Science Sichuan University Chengdu China National Engineering Research Center for Biomaterials School of Biomedical Engineering Sichuan University Chengdu China

Mass spectrometry (MS) serves as a powerful analytical technique in metabolomics. Traditional MS analysis workflows are heavily reliant on operator experience and are prone to be influenced by complex, high-dimensional MS data. This study introduces a deep learning framework designed to enhance the classification of complex MS data and facilitate biomarker screening. The proposed framework integrates preprocessing, classification, and biomarker selection, addressing challenges in high-dimensional MS analysis. Experimental results demonstrate significant improvements in classification tasks compared to other machine learning approaches. Additionally, the proposed peak-preprocessing module is validated for its potential in biomarker screening, identifying potential biomarkers from high-dimensional data. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Liver disease deep learning mass spectrometry pre-processing

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Magnetic nanoparticle detection methods in the context of complex fluids

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International Journal of Coal science & Technology 2024年第6期11卷 173-184页

作者： Alirza Orujov Jon M.Pikal TeYu Chien Jefferson A.Carter Saman A.Aryana Deptartment of Chemical and Biomedical Engineering University of WyomingLaramieWYUSA Deptartment of Electrical Engineering and Computer Science University of WyomingLaramieWYUSA Deptartment of Physics and Astronomy University of WyomingLaramieWYUSA

Foams improve mobility control in injection operations within geological ***,such as iron-oxide,have been shown to enhance the stability of foams when combined with *** this research,we leverage the magnetic properties of these nanoparticles to detect their presence as a surrogate for monitoring the geologic extent of injected fluids in the *** feasibility of using these nanoparticles for monitoring purposes stems from their detectability at low concentrations in subsurface *** developed two distinct methods to detect the presence of magnetite nanoparticles in complex *** simulate complex subsurface fluids in a laboratory setting,we included various ions and surfactants and investigated their effects on the detection of *** this end,we designed an experimental setup and tested two magnetic detection methods:Induction Heating(IH)and Oscillator Frequency Shift(OFS).The IH method involves applying a high-frequency alternating magnetic field to a solution containing small amounts of magnetic nanoparticles and measuring the temperature *** built an experimental setup to generate this magnetic field for different samples,with temperature changes recorded by an infrared *** results indicate that nanoparticle concentrations linearly affect the solution's temperature ***,the presence of ions and surfactants also influences the temperature *** OFS method measures shifts in the resonance frequency of a circuit caused by changes in magnetic permeability inside a *** coil is part of a transistor oscillator circuit that produces a sinusoidal voltage waveform,with the oscillation frequency depending on the coil’s *** presence of nanoparticles causes a shift in resonance frequency,which were precisely measured for various *** drop in resonance frequency is a linear function of nanoparticle concentration,and both methods detect concentrations as low as 15

关键词： CO_(2) storage Monitoring Nanoparticle-stabilized foams Sequestration Subsurface Tracer nanoparticles

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SSVEP-DAN: Cross-Domain Data Alignment for SSVEP-Based Brain-computer Interfaces

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IEEE Transactions on Neural Systems and Rehabilitation engineering 2024年 32卷 2027-2037页

作者： Chen, Sung-Yu Chang, Chi-Min Chiang, Kuan-Jung Wei, Chun-Shu Department of Computer Science Hsinchu30010 Taiwan Arctop Inc. La Jolla CA92093 United States Department of Computer Science The Institute of Education The Institute of Biomedical Engineering Hsinchu30010 Taiwan

Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency is highly dependent on individual training data acquired during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we introduce SSVEP-DAN, the first dedicated neural network model designed to align SSVEP data across different domains, encompassing various sessions, subjects, or devices. Our experimental results demonstrate the ability of SSVEP-DAN to transform existing source SSVEP data into supplementary calibration data. This results in a significant improvement in SSVEP decoding accuracy while reducing the calibration time. We envision SSVEP-DAN playing a crucial role in future applications of high-performance SSVEP-based BCIs. The source code for this work is available at: https://***/CECNL/SSVEP-DAN. © 2001-2011 IEEE.

关键词： Brain computer interface

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